Dense regions in networks contain unusual and interesting information [1]}.
Dense subgraph discovery is shown to be an effective analysis method in many applications across various domains [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}.
It is often a good and cheaper proxy for graph clustering because cohesive subgraphs in real-world networks exhibit good cuts [2]}, [1]}.
However, most algorithms to find dense subgraphs are designed for simple, undirected, unweighted graphs.
In reality, a common characteristic of natural and engineered systems from various domains is that the nodes (entities) and edges (relationships) have rich information associated with them; i.e., networks are heterogeneous [11]}.
Relationships can be asymmetrical (one-way) and entities/relationships can be associated with categorical and numerical attributes; e.g., length of a road or gender of a person.
Finding the dense subgraphs while actively considering the rich information on nodes/edges has various applications, such as entity resolution [12]} and link prediction [13]}.
